Background & Context

CoxianDistribution[{α1,…,αm-1},{λ1,…,λm}] represents a continuous statistical distribution defined over the interval , parameterized by two vectors (α1,…,αm-1) and (λ1,…,λm), and known as an -phase Coxian distribution. The parameters αi are called "phase probabilities" and have values in the interval , while the parameters λi are called "phase rates" and have positive real values. Together, these parameters determine the overall shape of the probability density function (PDF) and, depending on their values, the PDF may be monotonic decreasing or unimodal. In addition, the tails of the PDF are "thin" in the sense that the PDF decreases exponentially rather than decreasing algebraically for large values of . (This behavior can be made quantitatively precise by analyzing the SurvivalFunction of the distribution.) Random variables satisfying XCoxianDistribution[{α1,…,αm-1},{λ1,…,λm}] are sometimes said to have a Coxian distribution of order .

While the foundations of Coxian distributions originate with the work of mathematician D. R. Cox in the 1950s, much of the current corpus of knowledge was established through work on generalizations of hyperexponential distributions dating from the 1980s. To be mathematically precise, a random variable has a Coxian distribution of order if it starts in phase 1 and goes through no more than exponential phases where, for the phase (which has mean length equal to ), continues to phase i+1 with probability αi and finishes with probability 1-αi. A number of real-world phenomena behave in a way naturally modeled by a Coxian distribution, including teletraffic in mobile cellular networks, durations of stay among patients in geriatric facilities, and queueing systems of various types.

RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a Coxian distribution. Distributed[x,CoxianDistribution[{α1,…,αm-1},{λ1,…,λm}]], written more concisely as xCoxianDistribution[{α1,…,αm-1},{λ1,…,λm}], can be used to assert that a random variable x is distributed according to a Coxian distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.

The probability density and cumulative distribution functions may be given using PDF[CoxianDistribution[{α1,…,αm-1},{λ1,…,λm}],x] and CDF[CoxianDistribution[{α1,…,αm-1},{λ1,…,λm}],x]. The mean, median, variance, raw moments, and central moments may be computed using Mean, Median, Variance, Moment, and CentralMoment, respectively.

DistributionFitTest can be used to test if a given dataset is consistent with a Coxian distribution, EstimatedDistribution to estimate a Coxian parametric distribution from given data, and FindDistributionParameters to fit data to a Coxian distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic Coxian distribution and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic Coxian distribution.

TransformedDistribution can be used to represent a transformed Coxian distribution, CensoredDistribution to represent the distribution of values censored between upper and lower values, and TruncatedDistribution to represent the distribution of values truncated between upper and lower values. CopulaDistribution can be used to build higher-dimensional distributions that contain a Coxian distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving Coxian distributions.